Examples with MultiLayerConfiguration org.deeplearning4j.nn.conf.MultiLayerConfiguration used on opensource projects

Example 6 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration in project deeplearning4j by deeplearning4j.

the class GravesLSTMTest method testSingleExample.

@Test
public void testSingleExample() {
    Nd4j.getRandom().setSeed(12345);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).learningRate(0.1).seed(12345).list().layer(0, new org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder().activation(Activation.TANH).nIn(2).nOut(2).build()).layer(1, new org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(2).nOut(1).activation(Activation.TANH).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    INDArray in1 = Nd4j.rand(new int[] { 1, 2, 4 });
    INDArray in2 = Nd4j.rand(new int[] { 1, 2, 5 });
    in2.put(new INDArrayIndex[] { NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 4) }, in1);
    assertEquals(in1, in2.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 4)));
    INDArray labels1 = Nd4j.rand(new int[] { 1, 1, 4 });
    INDArray labels2 = Nd4j.create(1, 1, 5);
    labels2.put(new INDArrayIndex[] { NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 4) }, labels1);
    assertEquals(labels1, labels2.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 4)));
    INDArray out1 = net.output(in1);
    INDArray out2 = net.output(in2);
    System.out.println(Arrays.toString(net.output(in1).data().asFloat()));
    System.out.println(Arrays.toString(net.output(in2).data().asFloat()));
    List<INDArray> activations1 = net.feedForward(in1);
    List<INDArray> activations2 = net.feedForward(in2);
    for (int i = 0; i < 3; i++) {
        System.out.println("-----\n" + i);
        System.out.println(Arrays.toString(activations1.get(i).dup().data().asDouble()));
        System.out.println(Arrays.toString(activations2.get(i).dup().data().asDouble()));
        System.out.println(activations1.get(i));
        System.out.println(activations2.get(i));
    }
    //Expect first 4 time steps to be indentical...
    for (int i = 0; i < 4; i++) {
        double d1 = out1.getDouble(i);
        double d2 = out2.getDouble(i);
        assertEquals(d1, d2, 0.0);
    }
}

Example 7 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration in project deeplearning4j by deeplearning4j.

the class GravesLSTMTest method testGateActivationFnsSanityCheck.

@Test
public void testGateActivationFnsSanityCheck() {
    for (String gateAfn : new String[] { "sigmoid", "hardsigmoid" }) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).seed(12345).list().layer(0, new org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder().gateActivationFunction(gateAfn).activation(Activation.TANH).nIn(2).nOut(2).build()).layer(1, new org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(2).nOut(2).activation(Activation.TANH).build()).build();
        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();
        assertEquals(gateAfn, ((org.deeplearning4j.nn.conf.layers.GravesLSTM) net.getLayer(0).conf().getLayer()).getGateActivationFn().toString());
        INDArray in = Nd4j.rand(new int[] { 3, 2, 5 });
        INDArray labels = Nd4j.rand(new int[] { 3, 2, 5 });
        net.fit(in, labels);
    }
}

Example 8 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration in project deeplearning4j by deeplearning4j.

the class BatchNormalizationTest method checkMeanVarianceEstimateCNN.

@Test
public void checkMeanVarianceEstimateCNN() throws Exception {
    Nd4j.getRandom().setSeed(12345);
    //Check that the internal global mean/variance estimate is approximately correct
    //First, Mnist data as 2d input (NOT taking into account convolution property)
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.RMSPROP).seed(12345).list().layer(0, new BatchNormalization.Builder().nIn(3).nOut(3).eps(1e-5).decay(0.95).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).weightInit(WeightInit.XAVIER).activation(Activation.IDENTITY).nOut(10).build()).backprop(true).pretrain(false).setInputType(InputType.convolutional(5, 5, 3)).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    int minibatch = 32;
    List<DataSet> list = new ArrayList<>();
    for (int i = 0; i < 100; i++) {
        list.add(new DataSet(Nd4j.rand(new int[] { minibatch, 3, 5, 5 }), Nd4j.rand(minibatch, 10)));
    }
    DataSetIterator iter = new ListDataSetIterator(list);
    INDArray expMean = Nd4j.valueArrayOf(new int[] { 1, 3 }, 0.5);
    //Expected variance of U(0,1) distribution: 1/12 * (1-0)^2 = 0.0833
    INDArray expVar = Nd4j.valueArrayOf(new int[] { 1, 3 }, 1 / 12.0);
    for (int i = 0; i < 10; i++) {
        iter.reset();
        net.fit(iter);
    }
    INDArray estMean = net.getLayer(0).getParam(BatchNormalizationParamInitializer.GLOBAL_MEAN);
    INDArray estVar = net.getLayer(0).getParam(BatchNormalizationParamInitializer.GLOBAL_VAR);
    float[] fMeanExp = expMean.data().asFloat();
    float[] fMeanAct = estMean.data().asFloat();
    float[] fVarExp = expVar.data().asFloat();
    float[] fVarAct = estVar.data().asFloat();
    //        System.out.println("Mean vs. estimated mean:");
    //        System.out.println(Arrays.toString(fMeanExp));
    //        System.out.println(Arrays.toString(fMeanAct));
    //
    //        System.out.println("Var vs. estimated var:");
    //        System.out.println(Arrays.toString(fVarExp));
    //        System.out.println(Arrays.toString(fVarAct));
    assertArrayEquals(fMeanExp, fMeanAct, 0.01f);
    assertArrayEquals(fVarExp, fVarAct, 0.01f);
}

Example 9 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration in project deeplearning4j by deeplearning4j.

the class BatchNormalizationTest method checkSerialization.

@Test
public void checkSerialization() throws Exception {
    //Serialize the batch norm network (after training), and make sure we get same activations out as before
    // i.e., make sure state is properly stored
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(2).seed(12345).list().layer(0, new ConvolutionLayer.Builder().nIn(1).nOut(6).weightInit(WeightInit.XAVIER).activation(Activation.IDENTITY).build()).layer(1, new BatchNormalization.Builder().build()).layer(2, new ActivationLayer.Builder().activation(Activation.LEAKYRELU).build()).layer(3, new DenseLayer.Builder().nOut(10).activation(Activation.LEAKYRELU).build()).layer(4, new BatchNormalization.Builder().build()).layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nOut(10).build()).backprop(true).pretrain(false).setInputType(InputType.convolutionalFlat(28, 28, 1)).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    DataSetIterator iter = new MnistDataSetIterator(16, true, 12345);
    for (int i = 0; i < 20; i++) {
        net.fit(iter.next());
    }
    INDArray in = iter.next().getFeatureMatrix();
    INDArray out = net.output(in, false);
    INDArray out2 = net.output(in, false);
    assertEquals(out, out2);
    ByteArrayOutputStream baos = new ByteArrayOutputStream();
    ModelSerializer.writeModel(net, baos, true);
    baos.close();
    byte[] bArr = baos.toByteArray();
    ByteArrayInputStream bais = new ByteArrayInputStream(bArr);
    MultiLayerNetwork net2 = ModelSerializer.restoreMultiLayerNetwork(bais, true);
    INDArray outDeser = net2.output(in, false);
    assertEquals(out, outDeser);
}

Example 10 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration in project deeplearning4j by deeplearning4j.

the class BatchNormalizationTest method checkMeanVarianceEstimate.

@Test
public void checkMeanVarianceEstimate() throws Exception {
    Nd4j.getRandom().setSeed(12345);
    //Check that the internal global mean/variance estimate is approximately correct
    //First, Mnist data as 2d input (NOT taking into account convolution property)
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.RMSPROP).seed(12345).list().layer(0, new BatchNormalization.Builder().nIn(10).nOut(10).eps(1e-5).decay(0.95).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).weightInit(WeightInit.XAVIER).activation(Activation.IDENTITY).nIn(10).nOut(10).build()).backprop(true).pretrain(false).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    int minibatch = 32;
    List<DataSet> list = new ArrayList<>();
    for (int i = 0; i < 200; i++) {
        list.add(new DataSet(Nd4j.rand(minibatch, 10), Nd4j.rand(minibatch, 10)));
    }
    DataSetIterator iter = new ListDataSetIterator(list);
    INDArray expMean = Nd4j.valueArrayOf(new int[] { 1, 10 }, 0.5);
    //Expected variance of U(0,1) distribution: 1/12 * (1-0)^2 = 0.0833
    INDArray expVar = Nd4j.valueArrayOf(new int[] { 1, 10 }, 1 / 12.0);
    for (int i = 0; i < 10; i++) {
        iter.reset();
        net.fit(iter);
    }
    INDArray estMean = net.getLayer(0).getParam(BatchNormalizationParamInitializer.GLOBAL_MEAN);
    INDArray estVar = net.getLayer(0).getParam(BatchNormalizationParamInitializer.GLOBAL_VAR);
    float[] fMeanExp = expMean.data().asFloat();
    float[] fMeanAct = estMean.data().asFloat();
    float[] fVarExp = expVar.data().asFloat();
    float[] fVarAct = estVar.data().asFloat();
    //        System.out.println("Mean vs. estimated mean:");
    //        System.out.println(Arrays.toString(fMeanExp));
    //        System.out.println(Arrays.toString(fMeanAct));
    //
    //        System.out.println("Var vs. estimated var:");
    //        System.out.println(Arrays.toString(fVarExp));
    //        System.out.println(Arrays.toString(fVarAct));
    assertArrayEquals(fMeanExp, fMeanAct, 0.02f);
    assertArrayEquals(fVarExp, fVarAct, 0.02f);
}